Detecting infection of plant diseases by classifying plant photos

ABSTRACT

A system and processing methods for configuring and utilizing a convolutional neural network (CNN) for plant disease recognition are disclosed. In some embodiments, the system is programmed to collect photos of infected plants or leaves where regions showing symptoms of infecting diseases are marked. Each photo may have multiple marked regions. Depending on how the symptoms are sized or clustered, one marked region may include only one lesion caused by one disease, while another may include multiple, closely-spaced lesions caused by one disease. The system is programmed to determine anchor boxes having distinct aspect ratios from these marked regions for each convolutional layer of a single shot multibox detector (SSD). For certain types of plants, common diseases lead to relatively many aspect ratios, some having relatively extreme values. The system is programmed to then train the SSD using the marked regions and the anchor boxes and apply the SSD to new photos to identify diseased plants.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as aContinuation of application Ser. No. 16/658,021, filed Oct. 18, 2019,which claims the benefit under 35 U.S. C. § 119(e) of provisionalapplication 62/748,288, filed Oct. 19, 2018, the entire contents ofwhich are hereby incorporated by reference for all purposes as if fullyset forth herein. Applicant hereby rescinds any disclaimer of claimscope in the parent applications or the prosecution history thereof andadvises the USPTO that the claims in this application may be broaderthan any claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2020 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to the technical areas of plant diseasedetection and machine learning. The present disclosure also relates tothe technical area of improving configuration and training of machinelearning models for plant disease recognition.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Plant disease detection is important in agriculture. Today an automatedapproach often involves classifying plant photos, which can beimplemented by applying a convolutional neural network (CNN) having aplurality of convolutional layers. Some CNNs work together in atwo-stage approach, where the first CNN is used to propose regions ofinterest within given images and a second CNN is then used to classifyeach proposed region. Some CNNs require all images to be of a fixedsize. There are CNNs that can take images of various sizes and proposeand classify regions of interest in one shot with superior performance.Given the distinct symptoms of plant diseases, it would be helpful tospecifically configure and train such a CNN to classify plant photos anddetect infection of plant diseases to promote plant health and growth.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7A includes example photos of corn leaves each having symptoms ofone disease.

FIG. 7B includes an example photo of a corn leaf having symptoms ofmultiple diseases.

FIG. 8 illustrates an example process of applying non-maximumsuppression to refine an initial classification result to a finalclassification result.

FIG. 9 illustrates an example method performed by a server computer thatis programmed for configuring and utilizing a convolutional neuralnetwork for plant disease detection.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

-   -   1. GENERAL OVERVIEW    -   2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM        -   2.1. STRUCTURAL OVERVIEW        -   2.2. APPLICATION PROGRAM OVERVIEW        -   2.3. DATA INGEST TO THE COMPUTER SYSTEM        -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING        -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW    -   3. FUNCTIONAL DESCRIPTIONS        -   3.1 PLANT DISEASE DETECTION        -   3.2 DIGITAL MODEL CONFIGURATION        -   3.3 TRAINING SET AND DIGITAL MODEL CONSTRUCTION        -   3.4 DIGITAL MODEL EXECUTION        -   3.5 EXAMPLE PROCESSES    -   4. EXTENSIONS AND ALTERNATIVES

1. General Overview

A system and processing methods for configuring and utilizing aconvolutional neural network (CNN) for plant disease recognition aredisclosed. In some embodiments, the system is programmed to collectphotos of infected plants or leaves where regions showing symptoms ofinfecting diseases are marked. Each photo may have multiple markedregions. Depending on how the symptoms are sized or clustered, onemarked region may include only one lesion caused by one disease, whileanother may include multiple, closely-spaced lesions caused by onedisease. The system is programmed to determine anchor boxes (defaultboxes) having distinct aspect ratios from these marked regions for eachconvolutional layer of a single shot multibox detector (SSD). Forcertain types of plants, common diseases lead to relatively many aspectratios, some having relatively extreme values. The system is programmedto then train the SSD using the marked regions and the anchor boxes andapply the SSD to new photos to identify diseased plants.

In some embodiments, the system is programmed to collect marked photosof corn leaves. Each photo may have one or more marked regions (groundtruth boxes). Each marked region may have one or more lesions caused byone of a plurality of corn diseases and is labeled with the one corndisease. The system can be programmed to further process the markedregions. Specifically, the system can be programmed to break a markedregion into several or combine several marked regions into one based onhow regions corresponding to the lesions are sized or clustered in thephoto. For example, an expert might have marked in a photo two portionsof a big cluster of lesions caused by Southern Rust. The system can beprogrammed to merge and expand the two portions into one marked regioncovering the entire cluster because the regions corresponding todisconnected lesions in the cluster are spaced close together and thecluster covers up most of the leaf.

In some embodiments, the system is programmed to determine anchor boxesfor each of a series of convolutional layers of an SSD from theresulting marked regions. See Liu W. et al. (2016) SSD: Single ShotMultiBox Detector. In: Leibe B., Matas J., Sebe N., Welling M. (eds)Computer Vision—ECCV 2016, pp 21-37. Lecture Notes in Computer Science,vol 9905. Springer, Cham. The system can be programmed to normalize andcluster the marked regions and compute an aggregate for each cluster fordefining an anchor box. Each anchor box can thus have a distinct scaleand aspect ratio, representative of a subset of the marked regionsshowing symptoms of a common corn disease. For some corn diseases, theaspect ratio can range from 1/7 to 7.

In some embodiments, the system is programmed to map each marked regionto an anchor box based on the shape of the marked region. Specifically,the system can be configured to conclude a successful mapping when asuperimposition of the anchor box can cover more than a predefinedpercentage of the marked region. The system is programmed to then trainan SSD with images having the marked regions, the corresponding labels,the anchor boxes, and the mappings.

In some embodiments, the system is programmed to receive a new photo ofa corn leaf from a client device and apply the SSD to the new photo toreceive an initial identification of multiple regions in the photo and aclassification into one of the corn diseases for each of the multipleregions with a corresponding confidence score. When the same region isclassified into multiple corn diseases, some classifications associatedwith relatively low confidence scores can be filtered out. The system isprogrammed to then transmit the classification results to the clientdevice.

The system produces various technical benefits. An SSD has been shown toachieve better performance than similar CNNs, being faster than previoussingle shot detectors and also more accurate, in fact as accurate asslower techniques that perform explicit region proposals and pooling.The system provides an approach for configuring and training an SSD thatis especially suitable for classifying certain types of objects, such asplant disease symptoms. These symptoms comprise lesions that are sizedand positioned in specific manners, and the approach provided by thesystem captures that specificity and thus further improves performanceof the SSD in classifying plant disease symptoms. Such improvement inturn leads to better health and growth of crops.

Other aspects and features of embodiments will become apparent fromother sections of the disclosure.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U.S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5, the top two timelines have the “Spring applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 5,if the “Spring applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5, the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, agricultural intelligence computer system 130 isprogrammed to comprise a classification model management server computer(server) 170. The server 170 is further configured to comprise modelconfiguration instructions 172, model construction instructions 174,model execution instructions 176, and user interface instructions 178.

In some embodiments, the model configuration instructions 172 offercomputer-executable instructions to collect initial image data anddetermine values of certain parameters of a digital model forrecognizing plant diseases from the initial image data. When the digitalmodel is the SSD, the initial image data may be plant photos thatinclude marked regions each labeled with an identifier of a plantdisease. The marked regions can be further processed with respect to thefull images in building a training set for the SSD. The initial imagedata can also be simply the marked regions with corresponding labels.The relevant parameters of the SSD include a group of anchor boxes. Themodel configuration instructions 172 offer computer-executableinstructions to specifically build anchor boxes that represent symptomsof plant diseases.

In some embodiments, the model construction instructions 174 offercomputer-executable instructions to construct the training set and trainthe digital model with the training set. The training set can includeimages of a certain size having the marked regions and correspondinglabels or cropped, padded, or scaled versions of the images havingequivalent marked regions and and corresponding labels. When the digitalmodel is the SSD, the training set also includes mappings of the markedregions to the anchor boxes.

In some embodiments, the model execution instructions 176 offercomputer-executable instructions to apply the digital model to newimages for classification. The new image can be a new plant photoshowing symptoms of one or more plant diseases in one or more regions.The new image may need to be similarly cropped, padded, or scaled beforebeing fed into the digital model. When the digital model is the SSD,application of the digital model is expected to produce at least oneclassification into a certain plant disease for each of the one or moreregions.

In some embodiments, the user interface instructions 178 offercomputer-executable instructions to manage communications with otherdevices. The communications may include receiving the initial image dataincluding the labels from an image source, receiving a new photo forclassification from a client device, sending classification results forthe new photo to the client device, or sending digital data representingthe SSD to another client device.

Each component of the server 170 comprises a set of one or more pages ofmain memory, such as RAM, in the agricultural intelligence computersystem 130 into which executable instructions have been loaded and whichwhen executed cause the agricultural intelligence computing system toperform the functions or operations that are described herein withreference to those modules. For example, the model configuration module172 may comprise a set of pages in RAM that contain instructions whichwhen executed cause performing the location selection functions that aredescribed herein. The instructions may be in machine executable code inthe instruction set of a CPU and may have been compiled based uponsource code written in JAVA, C, C++, OBJECTIVE-C, or any otherhuman-readable programming language or environment, alone or incombination with scripts in JAVASCRIPT, other scripting languages andother programming source text. The term “pages” is intended to referbroadly to any region within main memory and the specific terminologyused in a system may vary depending on the memory architecture orprocessor architecture. In another embodiment, each component of theserver 170 also may represent one or more files or projects of sourcecode that are digitally stored in a mass storage device such asnon-volatile RAM or disk storage, in the agricultural intelligencecomputer system 130 or a separate repository system, which when compiledor interpreted cause generating executable instructions which whenexecuted cause the agricultural intelligence computing system to performthe functions or operations that are described herein with reference tothose modules. In other words, the drawing figure may represent themanner in which programmers or software developers organize and arrangesource code for later compilation into an executable, or interpretationinto bytecode or the equivalent, for execution by the agriculturalintelligence computer system 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. patent application Ser. No. 15/551,582, filed on Aug. 16, 2017, maybe used, and the present disclosure assumes knowledge of those patentdisclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example-Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Functional Descriptions

3.1 Plant Disease Detection

Today, a variety of classification methods based on image analysis areavailable. These classification methods can be used to analyze photos ofplants and classify the photos into given disease classes or a healthyclass, thereby detecting potential infection of the plants by thecorresponding diseases. Some of these classification methods involveCNNs, including the SSD.

Generally, an SSD starts with a base CNN comprising a series ofconvolutional layers that correspond to receptive fields of differentscales. The SSD then performs classification using feature maps producedby not only the last convolutional layer but also the otherconvolutional layers. More specifically, the SSD utilizes a set ofuser-defined anchor boxes, which typically have different sizes andaspect ratios that correspond to various features of given classes, foreach of those convolutional layers in the base CNN. The SSD thenincorporates a set of 4+c small (e.g., 3×3) filters for each of theanchor boxes (in a so-called convolutional feature layer), with 4corresponding to four sides of an anchor box and c being the number ofclasses, for each of the convolutional layers. These small filters aretrained with ground truth images of known features for each of theclasses. These ground truth images can have various sizes and aspectratios, and each image is associated with a class and an anchor box.These small filters can then be used to determine whether an areadelineated by one of the anchor boxes in a feature map produced by thecorresponding convolutional layer matches features of one of theclasses.

As described above, the SSD can recognize various features of theclasses having various scales and aspect ratios in one or more imagesand classify portions of the images into the classes with associatedconfidence scores accordingly in a single shot without requiring aninitial, separate round of region proposal to locate the features in theimages. It is possible that the SSD initially classifies a region of animage into multiple classes. Non-maximum suppression (NMS) can beapplied to select one of the multiple classes as the finalclassification. The SSD has been shown to be faster than previous singleshot detectors and also more accurate, in fact as accurate as slowertechniques that perform explicit region proposals and pooling.Third-party libraries that implement SSD-related functions using theKeras library and Python are available on the GitHub platform, which mayuse the VGG16 as the base CNN, for example.

3.2 Digital Model Configuration

In some embodiments, the server 170 is programmed to construct a digitalmodel for detecting infection of plant diseases from plant photos usingthe SSD. For corn, the common plant diseases include Anthracnose LeafBlight (ALB), Common Rust (CR), Eyespot (EYE), Gray Leaf Spot (GLS),Goss's Wilt (GW), Northern Leaf Blight (NLB), Northern Leaf Spot (NLS),Southern Leaf Blight (SLB), and Southern Rust (SR). The digital modelcan be designed to classify images into a certain number of classescorresponding to a certain number of such plant diseases. The server 170is programmed to first receive a set of images, such as photos of cornleaves, which can have marked regions of disease symptoms or directlyfeature such disease symptoms.

FIG. 7A includes example photos of corn leaves each having symptoms ofone disease. The image 720 shows symptoms of GLS within the box(defining a marked region) 704 and within the box 706. The box 704labeled with “GLS” includes one lesion, while the box 706 also labeledwith “GLS” includes disconnected but closely-located lesions, which isnot uncommon. The box 704 and the box 706 have different sizes andsimilar but distinct aspect ratios. Having a single box 706 instead ofmultiple boxes for each of the disconnected lesions may increaseefficiency in training and executing the digital model. The image 722shows symptoms of SR within the box 708, which includes a large clusterof separate lesions as is often the case. The box 708 also has adistinct size and aspect ratio.

FIG. 7B includes an example photo of a corn leaf having symptoms ofmultiple diseases. The image 724 shows symptoms of GLS within the box710. The image 724 also shows symptoms of CR within the box 712, whichincludes many separate lesions. The box 710 labeled with “GLS” and thebox 712 labeled with “CR” each have a distinct size and aspect ratio.While the box 712 includes lesions of multiple diseases, as the lesionsfrom CR dominate, this marked region corresponding to the box 712 maystill serve as a good sample for CR.

In some embodiments, the server 170 is programmed to process each imagehaving marked regions subject to certain rules, which can complete orenhance the marking. The server 170 can be configured to break eachmarked region into multiple ones or combine multiple marked regions intoone in accordance with a restriction on a size of a marked region, adensity of a cluster of lesions, or a size proportion between a clusterand a leaf. For example, instead of marking the box 712, the image 724might have had just a small marked region for an individual CR lesion.The server 170 can be programmed to automatically extend that markedregion to the box 712 from detecting nearby lesions and determining thetotal size of the cluster of lesions relative to the size of the leaf.In addition, the server 170 can be configured to limit the number ofmarked regions in each image, such as no more than six, to simplify theSSD training process. For example, the server 170 can be programmed toautomatically reduce the number of marked regions in the image 724 fromthe current seven to six by deselecting the marked region having thesmallest size or one having a similar aspect ratio as another markedregion.

In some embodiments, the server 170 is programmed to define anchor boxesfor the convolutional layers in the SSD. Each anchor box can be definedin terms of a unit length, an aspect ratio, and a scaling factor. Forexample, the unit length can be ten pixels, the aspect ratio of width toheight can be 1.0, and a scaling factor can be 1, leading to an anchorbox of ten pixels in width and ten pixels in length. The aspect ratiocan be 2.0 instead, leading to an anchor box of twenty pixels in widthand ten pixels in length. The scaling factor can be 2.0 instead, leadingto an anchor box of twenty pixels in width and twenty pixels in length.The server 170 can be programmed to use the marked regions, such asthose corresponding to the boxes 704, 706, 708, 710, or 712, to guidethe definition of anchor boxes. The server 170 can be programmed tonormalize the marked regions (e.g., to a fixed distance between thecamera and the plant and a fixed camera resolution), groupsimilarly-sized ones into one cluster, and compute an aggregate size andaspect ratio for each cluster to determine the scaling factors andaspect ratios. As the series of convolutional layers in a base CNNtypically correspond to increasingly larger receptive fields, the servercan be programmed to assign increasingly larger scaling factors to theseries of convolutional layers. The server can also be programmed toutilize a scaling factor, such as 0.1, that is smaller than the scalingfactors used in a typical implementation of the SSD to help identifyvery small lesions produced by certain corn diseases or other very smalldisease symptoms. For example, the scaling factors can be [0.1, 0.2,0.37, 0.54, 0.71, 0.88, 1.05] for a series of six convolutional layers,with one scaling factor for all anchor boxes assigned to oneconvolutional layer. For corn diseases, the common aspect ratios include1.0/7.0, 1.0/5.0, 1.0/3.0, 0.5, 1.0, 2.0, 3.0, 5.0, or 7.0.

3.3 Training Set and Digital Model Construction

In some embodiments, the server 170 is programmed to scale, pad, orotherwise process the images to produce final images for the trainingset. For example, the max_crop_and_resize and random_pad_and_resizefunctions available on the GitHub platform can be adapted to generatevariants of the original images. The server 170 is programmed toassociate each variant with the marked regions and corresponding labelsas in the original image. For plant disease detection, the classescorrespond to plant diseases, and each label identifies one of the plantdiseases. The images do not need to be rotated to produce additionalimages for the training set when symmetric aspect ratios are used forthe anchor boxes. To detect infection of corn diseases, the number ofmarked regions can be at least 100 for each of the corn diseases.

In some embodiments, the server 170 is programmed to match the trainingset of images to the anchor boxes, as required for building an SSD. Forexample, the SSDBoxEncoder available on the GitHub platform can beadapted to also refer to the variants of the original images for suchmatching purposes, with pos_iou_threshold set to 0.5 andneg_iou_threshold set to 0.2. The server 170 is programmed to then buildthe digital model for recognizing corn diseases from the bounding boxesand the training set, including images with the marked regions or theirvariants, the associated class labels, and the associated matches to theanchor boxes. For example, the model.fit_generator function in the Keraslibrary can be used with lr_schedule set to 0.001.

3.4 Digital Model Execution

In some embodiments, the server 170 is programmed to receive a newimage, such as a photo of a corn plant, and apply the digital model tothe new image. The server 170 is programmed to convert the new imageinto a square image as necessary. Instead of cropping the new imagecausing information loss, the server 170 can be configured to providepadding to create an updated image where each edge is as long as thelong edge of the new image. The server 170 can be configured to furthercenter the new image in the updated image and scale the result to obtaina final input image.

In some embodiments, the server 170 is programmed to then execute thedigital model on the final input image. For example, the decode_yfunction available on the GitHub platform can be used to implement theexecution, with confidence thresh set to 0.8 and iou_threshold set to0.5 for NMS.

FIG. 8 illustrates an example process of applying NMS to refine aninitial classification result to a final classification result. Theimage 802 is a photo of part of a corn leaf. As discussed above, the SSDmay initially classify a region of an image into multiple classes. Inthis example, each of the boxes, including the box 804 and the box 808,delineates an area of the image that has been classified into one of theclasses corresponding to corn diseases. In particular, the box 808 isamong a set of boxes that cover the pixel 806 or the surrounding region.Before application of the NMS, those boxes where the associatedconfidence scores are lower than a certain threshold, such as confidencethresh, can be filtered out. In this example, the classification of box804 is associated with a low confidence and thus can be removed. Throughthe NMS, the box with the largest score is then selected, all the otherboxes that overlap with that box for more than a particular threshold,such as iou_threshold, are then removed, and the process continues untilno more box can be removed. In this example, the box 808 is the only boxleft, and thus the pixel 806 will is classified based on the box 808.

3.5 Example Processes

FIG. 9 illustrates an example method performed by a server computer thatis programmed for configuring and utilizing a CNN for plant diseasedetection. FIG. 9 is intended to disclose an algorithm, plan or outlinethat can be used to implement one or more computer programs or othersoftware elements which when executed cause performing the functionalimprovements and technical advances that are described herein.Furthermore, the flow diagrams herein are described at the same level ofdetail that persons of ordinary skill in the art ordinarily use tocommunicate with one another about algorithms, plans, or specificationsforming a basis of software programs that they plan to code or implementusing their accumulated skill and knowledge.

In some embodiments, in step 902, the server 170 is programmed orconfigured to receive a set of photos of plants infected with aplurality of diseases. Specifically, the set of photos show leaves witha plurality of marked regions having multiple aspect ratios. Each markedregion is associated with a label of one of the plurality of diseasesand showing at least one lesion caused by the one disease. The set ofphotos includes a specific photo showing a specific leaf having aspecific marked region that shows multiple lesions. For any suchspecific photo, a total size of the multiple lesions would be greaterthan a first predefined percentage of a size of the specific markedregion. In addition, the size of the specific marked region would begreater than a second predefined percentage of a size of the specificleaf. For example, the set of photos would show infected corn leaves,one of the photos would show a leaf having a cluster of lesions for oneof the corn diseases, where the lesions are positioned close to oneanother and occupy a majority of the leaf.

In some embodiments, in step 904, the server 170 is programmed orconfigured to determine a group of anchor boxes from the plurality ofmarked regions for each of a series of convolutional layers of an SSD.Each of the group of anchor boxes generally has a distinct aspect ratiorepresentative of at least a subset of the plurality of marked regionsthat can correspond to similar symptoms of one disease. For corn, theaspect ratio can be as large as 7:1. The series of convolutional layerstend to have increasingly bigger receptive fields, therefore the server170 can be programmed to assign bigger anchor boxes to laterconvolutional layers.

In some embodiments, in step 906, after determining the group of anchorboxes, the server 170 can be programmed to further map each of theplurality of marked regions to one of the group of anchor boxes. Theserver can be configured to match a marked region with an anchor boxwhen a size percentage of an intersection over an union of the markedregion and the anchor box over the union is greater than a specificthreshold.

In some embodiments, in step 908, the server 170 is programmed orconfigured to build the SSD from the group of anchor boxes, the set ofphotos with the plurality of marked regions, the associated plurality oflabels of diseases, and the associated plurality of mappings to anchorboxes. For training purposes, the set of original photos can beaugmented with variants obtained from cropping, padding, resizing, orperforming another image processing operation on the original photos.The variants are associated with equivalent marked regions andcorresponding labels as in the original photos. Even through these imageprocessing operations, the aspect ratios of the marked regions are to bepreserved and associated with the same mappings to the anchor boxes asin the original photos.

In some embodiments, in step 910, the server 170 is programmed orconfigured to receive a new image from a client device. The new imagecan be a photo of a plant that shows symptoms of one or more diseases inone or more regions. In step 912, the server 170 is programmed orconfigured to apply the SSD to the new image to identify those symptomsof the one or more diseases in the one or more regions of the new image.

In some embodiments, in step 914, the server 170 is programmed orconfigured to transmit data related to the one or more diseases or oneor more regions to the client device. The data can identify each of theone or more regions and one or more classifications of the region. Thedata can also include a confidence score for each of the one or moreclassifications into one of the plant diseases.

4. Extensions and Alternatives

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A system for configuring and utilizing deeplearning for plant disease recognition, comprising: a memory; aprocessor coupled to the memory and configured to perform: receiving aset of photos of plants showing leaves with a plurality of markedregions having multiple aspect ratios, each marked region beingassociated with a label of a disease of a plurality of diseases andshowing at least one lesion caused by the disease; determining, by theprocessor, a group of anchor boxes from the plurality of marked regionsfor each of a series of convolutional layers of a single shot multiboxdetector (SSD), the SSD configured to receive an image and assign eachof one or more areas of the image into at least one of a plurality ofclasses corresponding to the plurality of diseases, the group of anchorboxes having distinct aspect ratios and corresponding to variousfeatures of the plurality of classes; mapping each of the plurality ofmarked regions to one of the groups of anchor boxes, comprising matchinga marked region with an anchor box when a superimposition of the anchorbox covers more than a predefined percentage of the marked region;building the SSD from the group of anchor boxes, the set of photoshaving the plurality of marked regions, the associated plurality oflabels, and the associated plurality of mappings; receiving a new imagefrom a client device; applying the SSD to the new image to identifysymptoms of one or more diseases in one or more areas of the new image.2. The system of claim 1, the SSD comprising the series of convolutionallayers that correspond to receptive fields of different scales andperforming classification using feature maps produced by each of theseries of convolutional layers.
 3. The system of claim 1, the SSDcomprising 4+c filters for each of the group of anchor boxes, with cbeing a size of the plurality of classes corresponding to the pluralityof diseases.
 4. The system of claim 1, the SSD including a fixed numberof filters of a fixed size, for each of the group of anchor boxes, to beapplied to feature maps produced by each of the series of convolutionallayers.
 5. The system of claim 1, wherein one of the plurality of markedregions shows multiple disconnected lesions having different sizes oraspect ratios.
 6. The system of claim 1, the processor furtherconfigured to perform adding additional photos to the set of photos bycropping and resizing a copy of a first photo of the set of photos or byrandomly padding and resizing a copy of a second photo of the set ofphotos.
 7. The system of claim 1, the mapping comprising matching amarked region with one of the group of anchor boxes when a sizepercentage of an intersection over an union of the marked region and theanchor box over the union is greater than a specific threshold.
 8. Thesystem of claim 1, the mapping further comprising matching a markedregion with a negative anchor box outside the group of anchor boxes whena size percentage of an intersection over an union of the marked regionand the anchor box over the union is less than a specific threshold. 9.The system of claim 1, the processor further configured to perform:dividing, combining, or removing one or more of the plurality of markedregions to create a new set of marked regions in accordance with arestriction on a size of a marked region, on a size proportion of acluster of legions within a marked region to a leaf, or on a density oflegions within a marked region based on the predefined percentage, thedetermining being performed from the new set of marked regions.
 10. Thesystem of claim 1, the determining further comprising normalizing one ofthe plurality of marked regions based on a fixed distance between acamera and a plan and a fixed camera resolution.
 11. The system of claim1, the determining comprising deselecting a first marked region from theplurality of marked regions that has a smallest size or a size similarto a second marked region of the plurality of marked regions.
 12. Thesystem of claim 1, the determining comprising: clustering the pluralityof marked regions into multiple clusters; computing an aggregate regionfor a cluster of the multiple clusters; defining an anchor box of thegroup of anchor boxes based on the cluster.
 13. The system of claim 1,the determining comprising: identifying each of the group of anchorboxes by a unit length, an aspect ratio, and a scaling factor; assigninga smaller scaling factor to the group of anchor boxes for aconvolutional layer earlier in the series of convolutional layers andassigning a larger scaling factor to the group of anchor boxes for aconvolutional layer later in the series of convolutional layers.
 14. Thesystem of claim 13, the plants being corns, the aspect ratio being1.0/7.0, 1.0/5.0, 1.0/3.0, 0.5, 1.0, 2.0, 3.0, 5.0, or 7.0.
 15. Thesystem of claim 1, the receiving the new image comprising padding thenew image into a square shape and then scaling the new image in thesquare shape.
 16. The system of claim 1, the applying comprising, when aspecific area of the one or more areas of the new image is assigned tomultiple classes of the plurality of classes, performing non-maximumsuppression (NMS) to select one of the multiple classes.
 17. The systemof claim 1, the applying comprising assigning the one or more areas ofthe new image into one or more classes of the plurality of classescorresponding to the one or more diseases.
 18. A computer-implementedmethod of configuring and utilizing deep learning for plant diseaserecognition, comprising: receiving, by a processor, a set of photos ofplants showing leaves with a plurality of marked regions having multipleaspect ratios, each marked region being associated with a label of adisease of a plurality of diseases and showing at least one lesioncaused by the disease; determining, by the processor, a group of anchorboxes from the plurality of marked regions for each of a series ofconvolutional layers of a single shot multibox detector (SSD), the SSDconfigured to receive an image and assign each of one or more areas ofthe image into at least one of a plurality of classes corresponding tothe plurality of diseases, the group of anchor boxes having distinctaspect ratios and corresponding to various features of the plurality ofclasses; mapping each of the plurality of marked regions to one of thegroups of anchor boxes, comprising matching a marked region with ananchor box when a superimposition of the anchor box covers more than apredefined percentage of the marked region; building the SSD from thegroup of anchor boxes, the set of photos having the plurality of markedregions, the associated plurality of labels, and the associatedplurality of mappings; receiving a new image from a client device;applying the SSD to the new image to identify symptoms of one or morediseases in one or more areas of the new image.
 19. Thecomputer-implemented method of claim 18, wherein one of the plurality ofmarked regions shows multiple disconnected lesions having differentsizes or aspect ratios.
 20. The computer-implemented method of claim 18,the mapping further comprising matching a marked region with a negativeanchor box outside the group of anchor boxes when a size percentage ofan intersection over an union of the marked region and the anchor boxover the union is less than a specific threshold.